I had a similar question here, and got some really useful and simple answers using tidyverse. In the end a really robust approach was made, which I wrapped in a function and use regularly.
library(tidyverse)
baseline_table <- function(data, variables, grouping_var) {
data %>%
group_by(!!sym(grouping_var)) %>%
summarise(
across(
all_of(variables),
~ paste0(mean(.) %>% round(2), "(±", sd(.) %>% round(2), ")")
)
) %>% pivot_longer(
cols = -grouping_var,
names_to = "variable"
) %>% pivot_wider(
names_from = grouping_var
)
}
It takes three arguments, data, variables and the grouping_var - all of which are rather self explanatory.
Here is a test using mtcars with a 2 level and 3 level grouping var.
baseline_table(
data = mtcars,
variables = c("mpg", "hp"),
grouping_var = "am"
)
# A tibble: 2 x 3
variable `0` `1`
<chr> <chr> <chr>
1 mpg 17.15(±3.83) 24.39(±6.17)
2 hp 160.26(±53.91) 126.85(±84.06)
baseline_table(
data = mtcars,
variables = c("mpg", "hp"),
grouping_var = "cyl"
)
# A tibble: 2 x 4
variable `4` `6` `8`
<chr> <chr> <chr> <chr>
1 mpg 26.66(±4.51) 19.74(±1.45) 15.1(±2.56)
2 hp 82.64(±20.93) 122.29(±24.26) 209.21(±50.98)
It works out of the box, and are applicable to all data, below I used iris,
baseline_table(
data = iris,
variables = c("Sepal.Length", "Sepal.Width"),
grouping_var = "Species"
)
# A tibble: 2 x 4
variable setosa versicolor virginica
<chr> <chr> <chr> <chr>
1 Sepal.Length 5.01(±0.35) 5.94(±0.52) 6.59(±0.64)
2 Sepal.Width 3.43(±0.38) 2.77(±0.31) 2.97(±0.32)
Of course; some grouping variables are not directly suited for this. Namely cyl but it does serve as a good example though. but you can recode your grouping variables accordingly,
baseline_table(
data = mtcars %>% mutate(cyl = paste(cyl, "Cylinders", sep = " ")),
variables = c("mpg", "hp"),
grouping_var = "cyl"
)
# A tibble: 2 x 4
variable `4 Cylinders` `6 Cylinders` `8 Cylinders`
<chr> <chr> <chr> <chr>
1 mpg 26.66(±4.51) 19.74(±1.45) 15.1(±2.56)
2 hp 82.64(±20.93) 122.29(±24.26) 209.21(±50.98)
You can also modify the function to include descriptive strings, about the values,
baseline_table <- function(data, variables, grouping_var) {
# Generate the table;
tmpTable <- data %>%
group_by(!!sym(grouping_var)) %>%
summarise(
across(
all_of(variables),
~ paste0(mean(.) %>% round(2), "(±", sd(.) %>% round(2), ")")
)
) %>% pivot_longer(
cols = -grouping_var,
names_to = "variable"
) %>% pivot_wider(
names_from = grouping_var
)
# Generate Descriptives dynamically
tmpDesc <- tmpTable[1,] %>% mutate(
across(.fns = ~ paste("Mean (±SD)"))
) %>% mutate(
variable = ""
)
bind_rows(
tmpDesc,
tmpTable
)
}
Granted, this extension is a bit awkward - but it is nonetheless still robust. The output is,
# A tibble: 3 x 4
variable `4 Cylinders` `6 Cylinders` `8 Cylinders`
<chr> <chr> <chr> <chr>
1 "" Mean (±SD) Mean (±SD) Mean (±SD)
2 "mpg" 26.66(±4.51) 19.74(±1.45) 15.1(±2.56)
3 "hp" 82.64(±20.93) 122.29(±24.26) 209.21(±50.98)
Update: Ive rewritten the function for added flexibility as noted in the comments.
library(tidyverse)
baseline_table <- function(data, variables, grouping_var) {
data %>%
group_by(!!!syms(grouping_var)) %>%
summarise(
across(
all_of(variables),
~ paste0(mean(.) %>% round(2), "(±", sd(.) %>% round(2), ")")
)
) %>% unite(
"grouping",
all_of(grouping_var)
) %>% pivot_longer(
cols = -"grouping",
names_to = "variables"
) %>% pivot_wider(
names_from = "grouping"
)
}
It works in the same way, and outputs the same, unless there is more than one grouping_var,
baseline_table(
mtcars,
variables = c("hp", "mpg"),
grouping_var = c("am", "cyl")
)
# A tibble: 2 x 7
variables `0_4` `0_6` `0_8` `1_4` `1_6` `1_8`
<chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 hp 84.67(±19.66) 115.25(±9.18) 194.17(±33.36) 81.88(±22.66) 131.67(±37.53) 299.5(±50.2)
2 mpg 22.9(±1.45) 19.12(±1.63) 15.05(±2.77) 28.08(±4.48) 20.57(±0.75) 15.4(±0.57)
In the updated function I used unite with a default seperator. Clearly, you can modify this to suit your needs such that the colnames says, for example, 4 Cylinder (Automatic) 6 Cylinder (Automatic) etc.
